| Paper: | SPTM-L6.6 | ||
| Session: | System Identification | ||
| Time: | Thursday, May 18, 11:40 - 12:00 | ||
| Presentation: | Lecture | ||
| Topic: | Signal Processing Theory and Methods: System Modeling, Representation, and Identification | ||
| Title: | The Variational Bayes Approximation in Bayesian Filtering | ||
| Authors: | Vaclav Smidl, Academy of Sciences of the Czech Republic, Czech Republic; Anthony Quinn, Trinity College Dublin, Ireland | ||
| Abstract: | The Variational Bayes (VB) approximation is applied in the context of Bayesian filtering, yielding a tractable on-line scheme for a wide range of non-stationary parametric models. This VB-filtering scheme is used to identify a Hidden Markov model with an unknown non-stationary transition matrix. In a simulation study involving soft-bit data, reliable inference of the underlying binary sequence is achieved in tandem with estimation of the transition probabilities. Its performance compares favourably with a proposed particle filtering approach, and at lower computational cost | ||
©2018 Conference Management Services, Inc. -||- email: webmaster@icassp2006.org -||- Last updated Friday, August 17, 2012